The author introduces MAGNET, a masked generative sequence modeling method for audio generation using a single non-autoregressive transformer. The approach combines efficient training with a novel rescoring method to enhance the quality of generated audio.
The author aims to improve audio generation diversity within specific categories by incorporating visual information, utilizing a clustering-based method. This approach enhances the quality and diversity of generated audios significantly.